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Constructivism Learning: A Learning ...

Li, Xiaoli.

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Constructivism Learning: A Learning Paradigm for Transparent Predictive Analytics.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Constructivism Learning: A Learning Paradigm for Transparent Predictive Analytics./
Author:	Li, Xiaoli.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	166 p.
Notes:	Source: Dissertations Abstracts International, Volume: 79-10, Section: B.
Contained By:	Dissertations Abstracts International79-10B.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10747860
ISBN:	9780355778045

Constructivism Learning: A Learning Paradigm for Transparent Predictive Analytics.
Li, Xiaoli.

Constructivism Learning: A Learning Paradigm for Transparent Predictive Analytics. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 166 p.

Source: Dissertations Abstracts International, Volume: 79-10, Section: B.

Thesis (Ph.D.)--University of Kansas, 2018.

This item must not be added to any third party search indexes.

Aiming to achieve the learning capabilities possessed by intelligent beings, especially human, researchers in machine learning field have the long-standing tradition of bor- rowing ideas from human learning, such as reinforcement learning, active learning, and curriculum learning. Motivated by a philosophical theory called "constructivism", in this work, we propose a new machine learning paradigm, constructivism learning. The constructivism theory has had wide-ranging impact on various human learning theories about how human acquire knowledge. To adapt this human learning theory to the context of machine learning, we first studied how to improve leaning perfor- mance by exploring inductive bias or prior knowledge from multiple learning tasks with multiple data sources, that is multi-task multi-view learning, both in offline and lifelong setting. Then we formalized a Bayesian nonparametric approach using se- quential Dirichlet Process Mixture Models to support constructivism learning. To fur- ther exploit constructivism learning, we also developed a constructivism deep learning method utilizing Uniform Process Mixture Models.

ISBN: 9780355778045Subjects--Topical Terms:

523869
Computer science.

Constructivism Learning: A Learning Paradigm for Transparent Predictive Analytics.
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520 $a Aiming to achieve the learning capabilities possessed by intelligent beings, especially human, researchers in machine learning field have the long-standing tradition of bor- rowing ideas from human learning, such as reinforcement learning, active learning, and curriculum learning. Motivated by a philosophical theory called "constructivism", in this work, we propose a new machine learning paradigm, constructivism learning. The constructivism theory has had wide-ranging impact on various human learning theories about how human acquire knowledge. To adapt this human learning theory to the context of machine learning, we first studied how to improve leaning perfor- mance by exploring inductive bias or prior knowledge from multiple learning tasks with multiple data sources, that is multi-task multi-view learning, both in offline and lifelong setting. Then we formalized a Bayesian nonparametric approach using se- quential Dirichlet Process Mixture Models to support constructivism learning. To fur- ther exploit constructivism learning, we also developed a constructivism deep learning method utilizing Uniform Process Mixture Models.
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